An efficient grid algorithm for faster clustering using K medoids approach

Abstract

Clustering is the methodology to separate similar objects of data set in one cluster and dissimilar objects of data set in another cluster. K means and K medoids are most widely used Clustering algorithms for selecting group of objects for data sets. k means clustering has less time complexity than k medoids method, but k means clustering method suffers from extreme values. So, we have focused our view to k medoids clustering method. Conventional k-medoids clustering algorithm suffers from many limitations. We have done analysis on these limitations such as the problem of finding natural clusters, the dependency of output on the order of input data. In this paper we have proposed a new algorithm named Grid Multidimensional K medoids which is designed to overcome the above limitations and provide a faster clustering than K medoids.